Abstract

This dissertation is motivated by the lack of scalable methods for the analysis and synthesis of different large-scale complex systems appearing in electrical and computer engineering. The systems of interest in this work are power networks, analog circuits, antenna systems, communication networks and distributed control systems. By combining theories from control and optimization, the high-level objective is to develop new design tools and algorithms that explicitly exploit the physical properties of these practical systems (e.g., passivity of electrical elements or sparsity of network topology). To this end, the aforementioned systems are categorized intro three classes of systems, and then studied in Parts I, II, and III of this dissertation, as explained below:
Power networks: In Part I of this work, the operation planning of power networks using efficient algorithms is studied. The primary focus is on the optimal power flow (OPF) problem, which has been studied by the operations research and power communities in the past 50 years with little success. In this part, it is shown that there exists an efficient method to solve a practical OPF problem along with many other energy-related optimization problems such as dynamic OPF or security-constrained OPF. The main reason for the successful convexification of these optimization problems is also identified to be the physical properties of a power circuit, especially the passivity of transmission lines.
Circuits and systems: Motivated by different applications in power networks, electromagnetics and optics, Part II of this work studies the fundamental limits associated with the synthesis of a particular type of linear circuit. It is shown that the optimal design of the parameters of this type of circuit can be performed in polynomial time if the circuit is passive and there are sufficient number of controllable (unknown) parameters. This result introduces a trade-off between the design simplicity and the implementation complexity for an important class of linear circuits. As an application of this methodology, the design of smart antennas is also studied; the goal is to devise an intelligent wireless communication device in order to avoid co-channel interference, power consumption in undesired directions and security issues. Since the existing smart antennas are either hard to program or hard to implement, a new type of smart antenna is synthesized by utilizing tools from algebraic geometry, control, communications, and circuits, which is both easy to program and easy to implement.
Distributed computation: The first problem tackled in Part III of this work is a very simple type of distributed computation, referred to as quantized consensus, which aims to compute the average of a set of numbers using a distributed algorithm subject to a quantization error. It is shown that quantized consensus is reached by means of a recently proposed gossip algorithm, and the convergence time of the algorithm is also derived. The second problem studied in Part III is a more advanced type of distributed computation, which is the distributed resource allocation problem for the Internet. The existing distributed resource allocation algorithms aim to maximize the utility of the network only at the equilibrium point and ignore the transient behavior of the network. To address this issue, it is shown that optimal control theory provides powerful tools for designing distributed resource allocation algorithms with a guaranteed real-time performance.
The results of this work can all be integrated to address real-world interdisciplinary problems, such as the design of the next generation of the electrical power grid, named the Smart Grid.